Financial Engineering in Complex Dynamic Systems

ROSSITSA YALAMOVA

Dhillon School of Business

University of Lethbridge

4401 University Dr. W., Lethbridge

CANADA

Abstract: - This paper explores the dynamic nature of financial markets through the lens of complex adaptive

systems (CAS) theory, aiming to provide a comprehensive understanding of how financial markets deviate from

the Efficient Market Hypothesis in extreme events such as bubbles and crashes. Traditional economic models

often struggle to capture the intricate dynamics of 'self-organizing' financial markets, particularly the interaction

between supply and demand in the face of evolving risks. CAS theory offers a promising framework for modeling

asset prices, emphasizing the interconnectedness and adaptability of various agents within the system.

The literature review highlights the significance of CAS theory in understanding the collective adaptation that

emerges from interactions among heterogeneous agents. Notably, researchers such as Holland (1995) and

Axelrod (1997) have demonstrated how simple agent-level rules can lead to sophisticated, self-organizing

behaviors at the system level, resulting in more efficient outcomes.

This paper also discusses the pivotal role of financial engineering in enhancing the adaptive capacity of

socioeconomic systems under extreme stress. In an increasingly unpredictable world characterized by natural

disasters, economic crises, and other unforeseen events, risk management serves as a vital mechanism for

volatility mitigation and financial protection. By spreading risk collectively through hedging strategies, financial

engineering not only provides portfolio security but also contributes to the resilience of financial and economic

systems.

By merging insights from CAS theory and the role of financial engineering in increasing adaptive capacity, this

paper contributes to a more comprehensive understanding of the risk dynamics in financial markets impacting

economic activities. Financial engineering tools mitigate negative shocks and reduce the severity of recessionary

cycles. An attempt is made to explain how collective adaptation can lead to more efficient risk management and

pricing, ultimately helping policymakers, fund managers, and researchers navigate the complexities of modern

financial markets and fortify socioeconomic systems against extreme stressors.

Key-Words: - Complex Systems, Self-organization, Adaptation, Resilience, Risk Modelling

Received: June 22, 2022. Revised: August 17, 2023. Accepted: October 12, 2023. Published: November 28, 2023.

1 Introduction

Almost all human behavior has a large rational

component. Individuals frequently insure against

certain kinds of contingencies. Simon's concept of

"rationality as process" and "rationality as a product

of thought" has long been a cornerstone of traditional

economic theory. It posits that individuals make

decisions by systematically weighing the costs and

benefits of their choices, striving to maximize their

utility within the constraints of available information.

This framework has been pivotal in understanding

economic behavior and shaping policy decisions.

However, it has faced challenges in explaining real-

world human behavior, which often deviates from the

idealized rational agent portrayed in classical

economics.

The emergence of behavioral economics, while

scarce in the Journal of Political Economy, marked a

significant departure from the traditional view of

rationality, recognizing that human decision-making

is susceptible to cognitive biases, emotions, and

bounded rationality. These factors can lead

individuals to make choices that may not align with

strict economic rationality. The works of Nobel

laureates Daniel Kahneman and Richard Thaler have

been instrumental in advancing this field.

Kahneman's groundbreaking research on prospect

theory and biases, and Thaler's contributions to

understanding the importance of nudges and choice

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architecture, have reshaped our understanding of

economic behavior.

This paper explores the intersection of Simon's

rationality framework and insights from behavioral

economics, with a specific focus on the evolving

landscape of behavioral finance. It delves into the

implications of cognitive biases, emotional

influences, and bounded rationality on investment

decision-making, offering a glimpse into the status

quo of this exciting and evolving field. By bridging

the gap between Simon's rationality and behavioral

economics, this paper aims to shed light on the

complexities of human behavior in the context of

investment hedging strategies, ultimately

contributing to more effective policy and product

design in financial engineering.

2 Motivation

In the realm of insurance, the concept of "risk

pooling" is fundamental. Unlike individual

investment decisions, where individuals seek to

optimize their own returns, insurance operates on the

principle that risk is shared collectively among a pool

of policyholders. This mechanism allows individuals

to protect themselves from financial hardships

resulting from unexpected events. However,

traditional economic models often struggle to capture

the dynamic interplay of supply and demand in the

insurance market, especially in the face of evolving

risks in a turbulent world. Derivatives were

developed as instruments for hedging risk in financial

markets. Financial engineering can be viewed as the

insurance wing of the financial industry.

Complex Adaptive Systems (CAS) theory offers a

promising approach to modeling the dynamic nature

of financial markets, including extreme events that

are not ‘viable’ under the Efficient Market

Hypothesis. CAS theory views systems as composed

of numerous interconnected agents that adapt their

behavior in response to changing conditions. In the

context of financial markets, these agents could

include investors, traders, fund managers, regulators,

and even external factors like natural disasters or

economic crises. Literature in this area suggests that

CAS theory provides a more realistic framework for

understanding the dynamics of financial markets

compared to traditional equilibrium models. It

acknowledges the heterogeneity of agents, their

interactions, and their capacity to adapt in a

constantly changing environment.

One of the critical advantages of adopting a CAS

approach is the recognition of collective adaptation.

This concept goes beyond individual intelligence and

emphasizes the emergent properties that arise from

the interactions among agents within the system. In

the turbulent world of finance, where risks evolve,

and uncertainties abound, collective adaptation

becomes crucial. Research by authors like Holland

(1995) and Axelrod (1997) has highlighted how

simple agent-level rules can lead to the emergence of

sophisticated, self-organizing behaviors at the system

level, often resulting in more efficient outcomes.

Applying these insights to the financial market can

help us better understand how collective adaptation

can lead to improved risk management, pricing, and

overall market stability.

In summary, by integrating complex adaptive

systems theory into the study of financial markets, I

aim to develop a theoretical framework that

transcends the limitations of traditional models. This

approach recognizes the collective adaptation of

agents within the system, providing a more realistic

perspective on how financial assets' demand and

supply define price formation, leading to bubbles and

crashes in a dynamically self-organizing complex

system. Such a framework can offer valuable insights

for policymakers seeking to enhance resilience in the

financial system.

3 Complex Socio-Economic Systems

Humans are social creatures. We coalesce into

families, tribes, cities, and countries, creating

structures and pathways to govern ourselves.

Throughout history, we have introduced

institutions—religions or new forms of government,

for instance—to help us adapt and overcome

population growth and technological advances.

However, we currently lack the means to adapt to

modern technology, particularly social media and

artificial intelligence, which are threatening the

accepted beliefs, norms, and behaviors that underpin

modern societies.

Historically, human societies have developed various

economic structures that reached astonishing levels

of success but nevertheless ended in collapse.

Diamond (2005) and Tainter (1990) examine the

socio-economic development and collapse of many

civilizations in an attempt to elucidate the universal

principles of growth of complex societies and their

route to failure. The adaptive capacity of a complex

system will determine how resilient the system is in

extreme conditions. The collective adaptation

framework establishes links between social

integration strategies, social environments, and

problem structures, shaping how groups respond to

dynamic situations.

A complex system, in its development, should be able

to adapt to the changing environment. In this regard,

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different systems may possess varying levels of

potential for change (e.g., complicated engineered

systems hold very little such potential). Although

complex systems may have high potential for change,

they should also preserve their identity through

internal controls realized through connectivity. Such

controls at a higher, slower level of the hierarchical

structure maintain integrity, while innovation and

change enter the system at a lower, faster level. The

semi-autonomous levels of this hierarchy consist of

components increasing in size and decelerating in

speed. The degree of flexibility or rigidity of those

controls determines the system’s sensitivity to

perturbations. Overall, the adaptive capacity of the

system is contingent on its topological structure and

feedback controls, as well as the balance between

internal and external controls.

Potential states that can be achieved without

changing the identity of a system or the potential to

return to a previous state maintaining the same

structure and functional controls after either external

perturbation or an internally caused crash can both be

viewed as adaptive resilience type 1. This type of

resilience depends on the width of the stable attractor

in which the system operates. The potential of the

system to re-emerge with a new identity after a crisis

is defined as the ability of the system to move to a

new stable attractor or resilience type 2. Complex

networks and graph theory can be applied to define a

system’s identity with its components and

relationships. Our theoretical framework for

measuring resilience in complex economic systems

examines the role of connectivity, evaluation of

possible future states (while preserving identity) and

their probability, mechanisms for implementing

change, and paths to new stable attractors or

alternative domains of the same attractor. The self-

similarity exponent for the semi-autonomous levels

reveals the degree of agglomeration in the economy

and the level of entrepreneurship-related resilience.

3.1 Context

The economic turbulence and multiple financial

crises of recent years have revealed that our global

socio-economic system itself does not seem to

possess the resilience to fully recover even after

unprecedented levels of growth. Furthermore,

policymakers lack the means to avoid or mitigate the

outcomes of such critical downturns. Research on the

economic system, nested in the larger

socioecological system, unveils the properties and

characteristics of the whole system at a smaller scale

of hierarchical organization. Therefore, research

methods in complexity science, with the appropriate

settings for resilience measures, are considered

relevant for entrepreneurship research due to their

generalizability to complex system research at a

larger scale.

A complex system contains semi-autonomous levels

of variables with similar speed or spatial attributes,

self-organized by a small number of controlling

processes. The configuration of self-similarity at all

levels facilitates integrity in structure and dynamics.

Fast-moving/changing small components comprise

the lower levels, thus inventions and changes enter

the system from below. The level above includes

scaled-up versions of the lower level structures,

clinging to lower speed and averting destabilization.

This preserves the integrity of the system. The global

socio-economic system embedded in the

environment produces an integrated complex

dynamical system with three aggregate levels with

decreasing speed of change – economic, social,

environment. Integrity of the system can be preserved

if the feedback controlling processes between levels

keep the system in dynamic equilibrium/stability

domain. Inventions from below create opportunities;

experimentation generates and tests innovation

through the adaptive cycle of exploitation,

conservation, release, and reorganization (Holling

1973).

Understanding the scale and scope of changes across

such dramatic boundaries is difficult but vital. Elinor

Ostrom’s research focused on the socio-ecological

interface in the system. Her research is of ultimate

importance for the future of a world of exponentially

growing population and industrialization. The

problems stemming from the diminishing carrying

capacity of the earth cannot be tackled separately and

independently by corporations, industries, or

governmental entities. Making progress toward

sustainable development demands that we get

international decision-making right. The contentious

state of climate change thinking as it strives to gain

urgent priority status is just one example of how such

processes require more than a massing of facts.

Sustainable development requires focusing on the

underlying economic, demographic, political, and

environmental factors that currently limit adaptive

capacity and increase vulnerability to climate change.

Any investigation of sustainability must be premised

on the fact that the human economy is inescapably a

subsystem of the earth system, which is a coherent

but vastly complex and highly nonlinear biophysical,

planetary-scale circuit of energy and materials whose

operations we still do not sufficiently understand.

The key point is that ecological constraints, such as

the consequences of carbon dioxide emissions, which

are feeding back into the human economy in drastic

ways, can hardly be dismissed as economically-

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irrelevant “externalities.” From the ecological point

of view, “sustainable growth” is an oxymoron; and

yet sustainable abundance and prosperity are

perfectly feasible if the human and ecological

conditions for it are properly understood. If the

human economy is to be sustainable in this way, it

can only be so, at least on this planet, by virtue of the

way it interacts with the earth system as a whole.

However, there is abundant, indeed alarming,

scientific evidence that the human economy is

presently not even close to being sustainable in this

ecological sense (Homer-Dixon 2007; Barnosky et

al. 2012). One of the goals of today’s entrepreneurs

must be to bring human ingenuity to bear on the huge

economic challenges confronting our species today.

But the socio-economic spill-over impacts of such

activity on global sustainability must be managed to

maintain those levels of the system in a domain of

stability (or transforming into another domain of

stability) to preserve the integrity of the planetary

system.

Fig. 1 Levels of Interactions in a Socio-Economic

System Embedded in the Environment.

Within management thinking, sustainability ranges

from financial aspects (solvency and growth) to

operational 'greening' within the company. Business

organizations are part of a network of economic and

social institutions. Understanding the topology of

these networks and the dynamics (flow of resources)

between the members will allow us to detect

impending problems (such as crashes or recessions)

and recommend strategies for preventive or remedial

intervention. Research investigating the topology and

synchronization dynamics of traders' complex

networks before crashes (e.g., Yalamova 2011)

serves as an example of a complex network model at

a smaller scale that can be scaled up to the overall

financial system and ultimately to the global

economy.

The complex network of economic and social

institutions should be examined to provide insight

into the dynamics of the system to formulate

intervention strategies for resilience building. While

a collapse of the dominant socio-economic order of

democratic capitalism may not be imminent, signs of

trouble are obvious, such as growing income

inequality and mounting debt levels linked to the

frequency of recessionary cycles. Self-organized

criticality is characterized by a power-law

distribution of events around the phase boundary

(i.e., critical point and crash in a complex system). A

power-law (Pareto) distribution accurately describes

income inequality and the disappearance of the

middle class that supports the growth economy. Such

violations refute the assumptions of classic economic

theory, specifically equilibrium models and

predictability, requiring a new approach to

macroeconomic research.

The adaptive capacity of complex hierarchical

systems (not to be confused with top-down

authoritative control hierarchy) was described by

Simon (1974). As communication between levels is

maintained, interactions within levels can be

transformed without losing the integrity of the

system. This essentially describes the relevance of

multilevel/polycentric governance in a complex

system (Ostrom and Janssen, 2004).

The system approach model should reflect the

complex structure of the socio-economic system, the

transfer of resources to upper levels, and

transformation within levels that maintain the

integrity of the system. Boulding (1981) argues that

social structures come into being through activities

described as 'social organizers' that are divided into

three groups: (1) threat and fear of consequences, (2)

exchange and economic rewards, and (3) integrative

forces (values, norms, religious beliefs, etc.). The

power of these social organizers may be used to move

the system into a more resilient part of the adaptive

cycle, i.e., to increase the adaptive capacity of the

system and mitigate the 'creative destruction' labeled

by Schumpeter (1950).

3.2 Re-examining Mainstream Economic

Methods and Assumptions

In the context of a complex system approach to

economic theory, changes in methodology should

align with the ultimate goal of understanding the

dynamics of complex socio-economic systems and

how to build resilience. This involves considering the

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role of regulatory intervention and the polycentric

governance of socioeconomic systems.

There is a growing consensus that the failure of

mainstream economics to predict the collapse of

2008 and the subsequent failures in policy responses

have prompted the need for new economic thinking.

Such a failure to provide understanding and solid

theoretical explanations for real-world phenomena

necessitates a re-examination of its philosophical

tenets.

Classical economic theory, with its focus on

equilibrium models, gives very little attention to

instabilities and out-of-equilibrium dynamics.

According to the Chicago School of Economics, an

out-of-equilibrium "inefficient" market is

theoretically impossible, and bubbles are neither

predictable nor detectable. The paradigm of classical

economic theory relies on independent

(representative) agents, competitive markets,

equilibrium models, additively aggregated variables,

and predictability. It fails to recognize the part/whole

relationships and nonlinearity that arise from

interconnectivity and complexity, leading to a

growing discrepancy with the reality it attempts to

model.

A more accurate approach to modeling economic

reality involves system thinking, considering

interconnected agents in a complex system of semi-

autonomous levels, ranging from sole proprietorships

and partnerships (SME) to small, medium, and large

corporations. Order emerges, and it is not

predetermined, featuring unpredictable, nonlinear,

and path-dependent dynamics. At each level of the

hierarchical structure, self-similar units possess

similar speed and singularity thresholds sustaining

dynamic equilibrium. Cross-scale interactions

(feedback control mechanisms) preserve the integrity

of the system.

Scientific progress in financial economics is hindered

by an over-reliance on econometric models as tools

of the dominant methodology. A shift analogous to

the transition from Newtonian to Einsteinian

gravitational theory is needed, recognizing the role of

philosophical interpretation in this transformation.

The reluctance of financial economists to engage in

philosophy of science discussions may stem from the

need to preserve implicit methodological standards,

despite explicit methodological arguments

supporting alternative approaches (e.g., behavioral

economics).

Arguments against prevailing economic

methodology reveal the necessity for substantial

modification of traditional methodological

conceptions and motivate alternative choices of

philosophical positions. Based on the dialectical law

of the passage of quantitative changes into qualitative

changes, the equilibrium concept's methodological

merits may be critiqued. As the theory does not

address out-of-equilibrium economics, it fails to

provide tools for the detection of instabilities. The

equilibrium concept, through the technique of

independent variables aggregation, eliminates

important aspects of interdependence and feedback

control, obscuring interesting parts of the theory that

might bear on disruptive change and resilience.

Quantifying complex systems aims to explain

emergent structures and self-organization. The

hierarchical structure and self-similarity of the

system create the potential for synchronization of

dynamics, leading to the famous "butterfly effect"

that may result in a collapse. Monitoring coupling

levels among subsystems and the process of

synchronization provides indications about the

stability of the system. Statistical complexity

measures, such as those proposed by Rosso et al.

(2010), characterize the system with its level of

disorder and its distance from equilibrium. These

measures offer quantitative methods for empirical

testing of instability indicators.

Recent work on understanding the potential for

disruptive change in complex public sector systems

argues for heterogeneity, adaptability, and learning

among agents. These agents, whether organizations

or institutions, interact at different levels, constituting

larger sectors such as industries or public sector

agglomerations. Managing the ability of sectors to

co-evolve is crucial for achieving the declared

outcomes of the larger system. Co-evolution in such

a complex system occurs both between agents

themselves and between agents and the external

environment. Recognizing and accounting for

competition for resources are essential, providing

adaptive tension that drives the system forward.

Research on complex adaptive systems (CAS)

suggests that they operate most effectively within a

range defined by two critical values based on the

amount of adaptive tension applied to the system.

Steering a CAS involves providing incentives to

move the system past the first critical value,

empowering self-organizational capabilities for

adaptation and innovation. However, it's crucial to be

aware that 'too much of a good thing' is also

dangerous, and damping mechanisms should be part

of the toolbox. McKelvey (2002) emphasizes the

relevance of damping mechanisms for policy

managers, considering the balance between

suppressing positive dynamics too quickly and not

suppressing negative dynamics quickly enough.

While the complexity toolbox can help guide systems

to adaptive novelty, the assumptions underlying

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those tools provide no great comfort for those seeking

predictable, reliable outcomes at a micro level. Co-

evolution is a mutually causal, deviation-amplifying,

positive feedback process where small initiating

events can have very large eventual impacts.

Damping can help, but there will be local surprises—

some good, some not so good. The risk-averse need

not apply!

3.3 Damping mechanisms

3.3.1. Loss of Agent Diversity:

A system adapts best when it contains as much

variety as its external environment (Ashby, 1958).

The tension between closure (strong ties) and

brokerage (weak ties) in networks is crucial for

adaptive growth, emphasizing the importance of

diversity. Successful organizations may fall into

competency traps, leading to homogeneity and the

need for frame-breaking creative destruction through

entrepreneurial initiatives.

3.3.2. Strength of Weak Ties:

The presence of weak ties between networks,

bridging 'two worlds,' is critical for the flow of novel

information and adaptive response. GE's 'simple-

rules' preventing 'best practice hoarding' and

promoting weak-tie construction fosters adaptive

tension and order creation (Kerr 2000).

3.3.3. Network Failure at the Nodes:

Deterioration of the capacity of nodes or agents

inhibits adaptation. Knowing 'who is good at what' is

crucial for adaptation, and open markets for talent

and ideas, common in entrepreneurial contexts,

minimize this potential deficit.

3.3.4. Separation from Adaptive Tension:

Productive co-evolution requires agents under

pressure to adapt to contextual problems.

Coevolutionary self-organization occurs when agents

engage with relevant drivers using boundary

spanners at the organization/environment interface.

Extreme adaptive tension in entrepreneurial

ecosystems, like Silicon Valley, poses challenges.

3.3.5. Self-organized Micro Defenses Against

Coevolution:

Organizations and systems may have coevolutionary

dynamics creating both beneficial and detrimental

order. Some 'rebel agents' may resist impending

change, turning coevolution itself into a damping

mechanism. Savvy organizations spin off rebel

agents while keeping a stake in their outcomes to

maximize entrepreneurial potential.

3.4 Moving toward Quantitative

Measures

The research agenda's next step involves producing

and applying quantitative measures for system

stability and resilience. Complexity statistical

measures help calculate entropy levels, indicating

instability fluctuations, and resilience, demonstrated

by the system's ability to recover after shocks. A

framework for empirical measurement of resilience,

presented in Cumming et al. (2005), suggests that

resilience is predictably related to connectivity.

3.5 An Integrative Strategy for Sustainability

3.5.1. Interconnected Subsystems:

Sustainability requires an integrative strategy

considering interconnected subsystems that do not

function independently. Results are affected by non-

linearity and feedback, necessitating a holistic

approach.

3.5.2. Globalization and Local Adaptation:

Globalization emphasizes the competitive advantage

of multi-unit organizations to use culturally sensitive

local adaptation, exemplified by Holton’s (2000)

hybridization thesis of globalization outcomes.

3.5.3. Flexibility and Openness:

Traditional business strategies focused on

predictability and clarity. Sustainable development

demands integrative strategies with flexibility,

openness, and high tolerance for disequilibrium.

3.5.4. Continuous Improvement and Trade-offs:

Integrative strategies must involve a heuristic

process, recognizing trade-offs and continuous

improvement as key principles. Effective

participation from all levels of enterprise and society

is essential.

3.6 Research in Integrative Management

Strategy:

Research in integrative management strategy should

go beyond measuring attractor-based resilience. An

appropriate model should compare viability

measures and resilience domain alternatives based on

policies of action, providing action plans without

assuming equilibrium in dynamics. This approach

considers the viability kernel's capture basin, offering

policy recommendations for resilience.

Mainstream thinking about equilibrium and

resilience in socio-economic systems needs

reevaluation. New frameworks and toolkits are under

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development, and efforts like ours aim to contribute

to these advancements.

4 Conclusion

4.1 Interdisciplinary Foundations:

The interdisciplinary effort aims to establish strong

theoretical foundations for research methodology.

The focus is on identifying the role of insurance as a

resilience component in sustaining socio-economic

systems.

4.2 Empirical Methods:

Empirical methods encompass statistical physics

measures, studying the complexity, topology, and

dynamics of complex system synchronization. These

methods provide a robust framework for

understanding the socio-economic impact of

integrated management strategies.

4.3 Resilience-Based Approaches:

Resilience-based approaches offer improved

alternatives to traditional 'command and control'

methods. The research emphasizes the need for

rigorous development of theory and empirical

methods to measure resilience and identify

mechanisms for change.

4.4 Polycentric Governance:

Management and polycentric governance are

proposed to address the diverse needs of multiple

stakeholders. Resilience at intermediate levels of

connectivity is highlighted as an indicator of network

robustness and fragility.

4.5 Shift Towards New Methods:

The shift aims to develop new methods for

synthesizing descriptions of complex socio-

economic dynamics across different enterprise

scales. This inclusive approach covers both Small

and Medium Enterprises (SME) and nascent

entrepreneurial entities.

4.6 Economic Modeling for Intervention:

Economic modeling within this framework provides

indicators of instabilities and tools for measuring the

effects of intervention policies. The goal is to

facilitate crash prevention and recovery, allowing for

the maintenance or restoration of desired pattern

dynamics in the system.

In summary, this comprehensive effort seeks to

redefine research methodologies, emphasizing the

importance of insurance as a resilience component in

sustaining socio-economic systems. The proposed

empirical methods and resilience-based approaches

offer promising avenues for advancing our

understanding of complex systems and developing

effective management strategies. The ultimate aim is

to enhance the adaptability and stability of socio-

economic systems across various scales and

conditions.

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The author contributed in the present research, at all

stages from the formulation of the problem to the

final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The author has no conflict of interest to declare that

is relevant to the content of this article.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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